Posters

Abstract

Reanalysis datasets are an essential component of wind energy yield assessment methodology nowadays, being routinely used to place short-term on-site measurements into a long-term context. The most widely used reanalyses in the industry at present are the ERA5 and MERRA-2 datasets which are available globally on an hourly basis with spatial resolutions in Europe of approximately 30 km and 50 km respectively, while the upcoming ERA6 reanalysis is expected to have an 18km spatial resolution. The Copernicus Regional Reanalysis for Europe (CERRA) is a dataset which was introduced originally in 2022 but has only recently been produced up to present date. It is available on an hourly basis with a spatial resolution of 5.5 km [1]. Industry adoption of the CERRA dataset has been limited so far, although early research indicates that it may offer similar or improved representation of site conditions compared to ERA5 and MERRA-2. While some validation work for the CERRA dataset has been published [2-5], this has not been exhaustive and gaps remain in terms of 1) using CERRA routinely in commercial energy production assessments, 2) its performance over a various regions across Europe, and 3) temporal consistency of the dataset. This study seeks to address these gaps by evaluating CERRA performance against a substantial number public and proprietary onshore and offshore measurement datasets. The analysis focuses on data consistency, long-term correlations at multiple temporal resolutions, and the accuracy of modelled wind speed frequency distributions in both long- and short-term context. Automation is key for this study and this analysis will make use of the Task-43 data model for normalizing disparate noisy measurement wind data and meta data. This study will both present the tool chain used in this comparison along with a public worked example on a limited number of datasets. The worked example will be in the form of a publicly hosted notebook. Early results indicate that CERRA generally exhibits similar or improved correlation with offshore measurements compared to ERA5 and MERRA-2, while results for onshore sites show less consistent improvement. The study is ongoing, and further datasets will be incorporated to strengthen the statistical robustness of the conclusions. [1] “Copernicus regional reanalysis for Europe (CERRA)” https://climate.copernicus.eu/copernicus-regional-reanalysis-europe-cerra, accessed 2026-01-20 [2] “Collocating wind data: A case study on the verification of the CERRA dataset”, F Rouholahnejad et al 2024 J. Phys.: Conf. Ser. 2875 012016 [3] “A Systematic Evaluation of the New European Wind Atlas and the Copernicus European Regional Reanalysis Wind Datasets in the Mediterranean Sea” Soukissian, T.; Apostolou, V.; Koutri, N.-E., J. Mar. Sci. Eng. 2025, 13, 1445. https://doi.org/10.3390/jmse13081445 [4] “Evaluation of ERA5, COSMO-REA6 and CERRA in simulating wind speed along the French coastline for wind energy applications”, Anindita Patra et al 2025, Adv. Sci. Res., 22, 69–85, 2025. https://doi.org/10.5194/asr-22-69-2025 [5] “Memo: Accuracy of Wind Speeds in Copernicus Regional Reanalysis for Europe, CERRA”, Morten Thøgersen et al, EMD International